{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "#skip\n",
    "! [ -e /content ] && pip install -Uqq fastai  # upgrade fastai on colab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Collaborative filtering tutorial\n",
    "\n",
    "> Using the fastai library for collaborative filtering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.tabular.all import *\n",
    "from fastai.collab import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all_slow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial highlights on how to quickly build a `Learner` and train a model on collaborative filtering tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this tutorial, we will use the [Movielens 100k data dataset](https://grouplens.org/datasets/movielens/100k/). We can download it easily and decompress it with the following function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = untar_data(URLs.ML_100k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main table is in `u.data`. Since it's not a proper csv, we have to specify a few things while opening it: the tab delimiter, the columns we want to keep and their names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>movie</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>186</td>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>377</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>244</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>166</td>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user  movie  rating\n",
       "0   196    242       3\n",
       "1   186    302       3\n",
       "2    22    377       1\n",
       "3   244     51       2\n",
       "4   166    346       1"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv(path/'u.data', delimiter='\\t', header=None,\n",
    "                      usecols=(0,1,2), names=['user','movie','rating'])\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Movie ids are not ideal to look at things, so we load the corresponding movie id to the title that is in the table `u.item`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie              title\n",
       "0      1   Toy Story (1995)\n",
       "1      2   GoldenEye (1995)\n",
       "2      3  Four Rooms (1995)\n",
       "3      4  Get Shorty (1995)\n",
       "4      5     Copycat (1995)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = pd.read_csv(path/'u.item',  delimiter='|', encoding='latin-1',\n",
    "                     usecols=(0,1), names=('movie','title'), header=None)\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we merge it to our ratings table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>movie</th>\n",
       "      <th>rating</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>226</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>154</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>306</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user  movie  rating         title\n",
       "0   196    242       3  Kolya (1996)\n",
       "1    63    242       3  Kolya (1996)\n",
       "2   226    242       5  Kolya (1996)\n",
       "3   154    242       3  Kolya (1996)\n",
       "4   306    242       5  Kolya (1996)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = ratings.merge(movies)\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then build a `DataLoaders` object from this table. By default, it takes the first column for user, the second column for the item (here our movies) and the third column for the ratings. We need to change the value of `item_name` in our case, to use the titles instead of the ids:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dls = CollabDataLoaders.from_df(ratings, item_name='title', bs=64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In all applications, when the data has been assembled in a `DataLoaders`, you can have a look at it with the `show_batch` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>title</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>181</td>\n",
       "      <td>Substitute, The (1996)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>189</td>\n",
       "      <td>Ulee's Gold (1997)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>L.A. Confidential (1997)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>849</td>\n",
       "      <td>Net, The (1995)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>435</td>\n",
       "      <td>Blade Runner (1982)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>718</td>\n",
       "      <td>My Best Friend's Wedding (1997)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>279</td>\n",
       "      <td>I Love Trouble (1994)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>561</td>\n",
       "      <td>Clockwork Orange, A (1971)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>87</td>\n",
       "      <td>Fish Called Wanda, A (1988)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>774</td>\n",
       "      <td>Crow, The (1994)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dls.show_batch()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fastai can create and train a collaborative filtering model by using `collab_learner`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = collab_learner(dls, n_factors=50, y_range=(0, 5.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It uses a simple dot product model with 50 latent factors. To train it using the 1cycle policy, we just run this command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.967653</td>\n",
       "      <td>0.942309</td>\n",
       "      <td>00:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.843426</td>\n",
       "      <td>0.869254</td>\n",
       "      <td>00:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.733788</td>\n",
       "      <td>0.823143</td>\n",
       "      <td>00:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.593507</td>\n",
       "      <td>0.811050</td>\n",
       "      <td>00:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.480942</td>\n",
       "      <td>0.811475</td>\n",
       "      <td>00:10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(5, 5e-3, wd=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's [some benchmarks](https://www.librec.net/release/v1.3/example.html) on the same dataset for the popular Librec system for collaborative filtering. They show best results based on RMSE of 0.91 (scroll down to the 100k dataset), which corresponds to an MSE of `0.91**2 = 0.83`. So in less than a minute, we got pretty good results!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpretation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's analyze the results of our previous model. We will keep the 1000 most rated movies for this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Star Wars (1977)', 'Contact (1997)', 'Fargo (1996)',\n",
       "       'Return of the Jedi (1983)', 'Liar Liar (1997)',\n",
       "       'English Patient, The (1996)', 'Scream (1996)', 'Toy Story (1995)',\n",
       "       'Air Force One (1997)', 'Independence Day (ID4) (1996)'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = ratings.groupby('title')['rating'].count()\n",
    "top_movies = g.sort_values(ascending=False).index.values[:1000]\n",
    "top_movies[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Movie bias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model has learned one bias per movie, a unique number independent of users that can be interpreted as the intrinsic \"value\" of the movie. We can grab the bias of each movie in our `top_movies` list with the following command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1000])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_bias = learn.model.bias(top_movies, is_item=True)\n",
    "movie_bias.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare those biases with the average ratings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ratings = ratings.groupby('title')['rating'].mean()\n",
    "movie_ratings = [(b, i, mean_ratings.loc[i]) for i,b in zip(top_movies,movie_bias)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's have a look at the movies with the worst bias:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(tensor(-0.3489),\n",
       "  'Children of the Corn: The Gathering (1996)',\n",
       "  1.3157894736842106),\n",
       " (tensor(-0.3407), 'Leave It to Beaver (1997)', 1.8409090909090908),\n",
       " (tensor(-0.3304), 'Cable Guy, The (1996)', 2.339622641509434),\n",
       " (tensor(-0.2763),\n",
       "  'Lawnmower Man 2: Beyond Cyberspace (1996)',\n",
       "  1.7142857142857142),\n",
       " (tensor(-0.2607), \"McHale's Navy (1997)\", 2.1884057971014492),\n",
       " (tensor(-0.2572), 'Grease 2 (1982)', 2.0),\n",
       " (tensor(-0.2482), 'Kansas City (1996)', 2.260869565217391),\n",
       " (tensor(-0.2479), 'Crow: City of Angels, The (1996)', 1.9487179487179487),\n",
       " (tensor(-0.2388), 'Free Willy 3: The Rescue (1997)', 1.7407407407407407),\n",
       " (tensor(-0.2338), 'Keys to Tulsa (1997)', 2.24),\n",
       " (tensor(-0.2305), 'Beautician and the Beast, The (1997)', 2.313953488372093),\n",
       " (tensor(-0.2205), 'Escape from L.A. (1996)', 2.4615384615384617),\n",
       " (tensor(-0.2192), 'Beverly Hills Ninja (1997)', 2.3125),\n",
       " (tensor(-0.2179), 'Mortal Kombat: Annihilation (1997)', 1.9534883720930232),\n",
       " (tensor(-0.2150), 'Thinner (1996)', 2.4489795918367347)]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item0 = lambda o:o[0]\n",
    "sorted(movie_ratings, key=item0)[:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or the ones with the best bias:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(tensor(0.6052), 'As Good As It Gets (1997)', 4.196428571428571),\n",
       " (tensor(0.5778), 'Titanic (1997)', 4.2457142857142856),\n",
       " (tensor(0.5565), 'Shawshank Redemption, The (1994)', 4.445229681978798),\n",
       " (tensor(0.5460), 'L.A. Confidential (1997)', 4.161616161616162),\n",
       " (tensor(0.5264), 'Silence of the Lambs, The (1991)', 4.28974358974359),\n",
       " (tensor(0.5125), 'Star Wars (1977)', 4.3584905660377355),\n",
       " (tensor(0.4862), \"Schindler's List (1993)\", 4.466442953020135),\n",
       " (tensor(0.4851), 'Rear Window (1954)', 4.3875598086124405),\n",
       " (tensor(0.4671), 'Godfather, The (1972)', 4.283292978208232),\n",
       " (tensor(0.4668), 'Apt Pupil (1998)', 4.1),\n",
       " (tensor(0.4614), \"One Flew Over the Cuckoo's Nest (1975)\", 4.291666666666667),\n",
       " (tensor(0.4606), 'Good Will Hunting (1997)', 4.262626262626263),\n",
       " (tensor(0.4572), 'Contact (1997)', 3.8035363457760316),\n",
       " (tensor(0.4529), 'Close Shave, A (1995)', 4.491071428571429),\n",
       " (tensor(0.4410), 'Wrong Trousers, The (1993)', 4.466101694915254)]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_ratings, key=lambda o: o[0], reverse=True)[:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is certainly a strong correlation!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Movie weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try to analyze the latent factors our model has learned. We can grab the weights for each movie in `top_movies` the same way as we did for the bias before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1000, 50])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_w = learn.model.weight(top_movies, is_item=True)\n",
    "movie_w.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try a PCA to reduce the dimensions and see if we can see what the model learned:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1000, 3])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_pca = movie_w.pca(3)\n",
    "movie_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fac0,fac1,fac2 = movie_pca.t()\n",
    "movie_comp = [(f, i) for f,i in zip(fac0, top_movies)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the highest score on the first dimension:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(tensor(1.1481), 'Casablanca (1942)'),\n",
       " (tensor(1.0816), 'Chinatown (1974)'),\n",
       " (tensor(1.0486), 'Lawrence of Arabia (1962)'),\n",
       " (tensor(1.0459), 'Wrong Trousers, The (1993)'),\n",
       " (tensor(1.0282), 'Secrets & Lies (1996)'),\n",
       " (tensor(1.0245), '12 Angry Men (1957)'),\n",
       " (tensor(1.0095), 'Some Folks Call It a Sling Blade (1993)'),\n",
       " (tensor(0.9874), 'Close Shave, A (1995)'),\n",
       " (tensor(0.9800), 'Wallace & Gromit: The Best of Aardman Animation (1996)'),\n",
       " (tensor(0.9791), 'Citizen Kane (1941)')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the worst:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(tensor(-1.2520), 'Home Alone 3 (1997)'),\n",
       " (tensor(-1.2118), 'Jungle2Jungle (1997)'),\n",
       " (tensor(-1.1282), 'Stupids, The (1996)'),\n",
       " (tensor(-1.1229), 'Free Willy 3: The Rescue (1997)'),\n",
       " (tensor(-1.1161), 'Leave It to Beaver (1997)'),\n",
       " (tensor(-1.0821), 'Children of the Corn: The Gathering (1996)'),\n",
       " (tensor(-1.0703), \"McHale's Navy (1997)\"),\n",
       " (tensor(-1.0695), 'Bio-Dome (1996)'),\n",
       " (tensor(-1.0652), 'Batman & Robin (1997)'),\n",
       " (tensor(-1.0627), 'Cowboy Way, The (1994)')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0))[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same thing for our second dimension:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_comp = [(f, i) for f,i in zip(fac1, top_movies)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(tensor(1.1196), 'Braveheart (1995)'),\n",
       " (tensor(1.0969), 'Raiders of the Lost Ark (1981)'),\n",
       " (tensor(1.0365), 'Independence Day (ID4) (1996)'),\n",
       " (tensor(0.9631), 'Titanic (1997)'),\n",
       " (tensor(0.9450), 'American President, The (1995)'),\n",
       " (tensor(0.8893), 'Forrest Gump (1994)'),\n",
       " (tensor(0.8757), 'Hunt for Red October, The (1990)'),\n",
       " (tensor(0.8638), 'Pretty Woman (1990)'),\n",
       " (tensor(0.8019), 'Miracle on 34th Street (1994)'),\n",
       " (tensor(0.7956), 'True Lies (1994)')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(tensor(-0.9231), 'Ready to Wear (Pret-A-Porter) (1994)'),\n",
       " (tensor(-0.8948), 'Dead Man (1995)'),\n",
       " (tensor(-0.8816), 'Clockwork Orange, A (1971)'),\n",
       " (tensor(-0.8697), 'Three Colors: Blue (1993)'),\n",
       " (tensor(-0.8425), 'Beavis and Butt-head Do America (1996)'),\n",
       " (tensor(-0.8047), 'Cable Guy, The (1996)'),\n",
       " (tensor(-0.7832), 'Nosferatu (Nosferatu, eine Symphonie des Grauens) (1922)'),\n",
       " (tensor(-0.7662), 'Exotica (1994)'),\n",
       " (tensor(-0.7546), 'Spice World (1997)'),\n",
       " (tensor(-0.7491), 'Heavenly Creatures (1994)')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0))[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can even plot the movies according to their scores on those dimensions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idxs = np.random.choice(len(top_movies), 50, replace=False)\n",
    "idxs = list(range(50))\n",
    "X = fac0[idxs]\n",
    "Y = fac2[idxs]\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.scatter(X, Y)\n",
    "for i, x, y in zip(top_movies[idxs], X, Y):\n",
    "    plt.text(x,y,i, color=np.random.rand(3)*0.7, fontsize=11)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "split_at_heading": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
